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                    大數據下數模聯動的隨機退化設備剩余壽命預測技術

                    李天梅 司小勝 劉翔 裴洪

                    李天梅, 司小勝, 劉翔, 裴洪. 大數據下數模聯動的隨機退化設備剩余壽命預測技術. 自動化學報, 2021, 45(x): 1?23 doi: 10.16383/j.aas.c201068
                    引用本文: 李天梅, 司小勝, 劉翔, 裴洪. 大數據下數模聯動的隨機退化設備剩余壽命預測技術. 自動化學報, 2021, 45(x): 1?23 doi: 10.16383/j.aas.c201068
                    Li Tian-Mei, Si Xiao-Sheng, Liu Xiang, Pei Hong. Data-model interactive remaining useful life prediction technologies for stochastic degrading devices with big data. Acta Automatica Sinica, 2021, 45(x): 1?23 doi: 10.16383/j.aas.c201068
                    Citation: Li Tian-Mei, Si Xiao-Sheng, Liu Xiang, Pei Hong. Data-model interactive remaining useful life prediction technologies for stochastic degrading devices with big data. Acta Automatica Sinica, 2021, 45(x): 1?23 doi: 10.16383/j.aas.c201068

                    大數據下數模聯動的隨機退化設備剩余壽命預測技術

                    doi: 10.16383/j.aas.c201068
                    基金項目: 國家自然科學基金(62073336, 61922089, 61773386)資助
                    詳細信息
                      作者簡介:

                      李天梅:火箭軍工程大學副教授, 主要研究方向為預測與健康管理, 剩余壽命智能預測. E-mail: tmlixjtu@163.com

                      司小勝:火箭軍工程大學教授, 主要研究方向為隨機退化系統剩余壽命預測與健康管理, 隨機退化建模, 預測維護. 本文通訊作者. E-mail: sxs09@mails.tsinghua.edu.cn

                      劉翔:火箭軍工程大學講師, 主要研究方向為預測與健康管理, 剩余壽命智能預測. E-mail: liux_92@163.com

                      裴洪:火箭軍工程大學講師, 主要研究方向為預測與健康管理, 剩余壽命智能預測. E-mail: ph2010hph@sina.com

                    Data-Model Interactive Remaining Useful Life Prediction Technologies for Stochastic Degrading Devices with Big Data

                    Funds: National Nature Science Foundation of P. R. China (62073336, 61922089, 61773386)
                    More Information
                      Author Bio:

                      LI Tian-Mei Associate professor in College of Missile Engineering, Rocket Force University of Engineering. Her research interest covers prognostics and health management, remaining useful life intelligent prediction

                      SI Xiao-Sheng Professor at the Rocket Force University of Engineering. His research interest covers remaining useful life prediction and health management, stochastic degradation modeling and predictive maintenance. The corresponding author of the paper

                      LIU Xiang Lecturer in College of Missile Engineering, Rocket Force University of Engineering. His research interest covers prognostics and health management, remaining useful life intelligent prediction

                      PEI Hong Lecturer in College of Missile Engineering, Rocket Force University of Engineering. His research interest covers prognostics and health management, remaining useful life intelligent prediction

                    • 摘要: 本文面向大數據背景下隨機退化設備剩余壽命預測的現實需求, 結合隨機退化設備監測大數據特點及剩余壽命預測不確定性量化這一核心問題, 深入分析了機理模型與數據混合驅動的剩余壽命預測技術、基于機器學習的剩余壽命預測技術、統計數據驅動的剩余壽命預測技術以及機器學習和統計數據驅動相結合的剩余壽命預測技術的基本研究思想和發展動態, 剖析了當前研究存在的局限性和共性難題. 針對存在的局限性和共性難題, 以多源傳感監測大數據下剩余壽命預測問題為例, 提出了一種數模聯動的大數據下隨機退化設備剩余壽命預測解決思路, 并通過航空發動機多源監測數據初步驗證了該思路的可行性和有效性. 最后, 借鑒數模聯動思路, 綜合考慮機器學習方法和統計數據驅動方法的優勢, 緊緊扭住大數據背景下隨機退化設備剩余壽命預測不確定性量化問題, 提出了大數據背景下深度學習與隨機退化建模交互聯動、監測大數據與剩余壽命及其預測不確定性映射機制、非理想大數據下的剩余壽命預測等亟待解決的關鍵科學問題.
                    • 圖  1  剩余壽命預測方法體系

                      Fig.  1  The methodology of remaining useful life prediction

                      圖  2  完整的、碎片化的、稀疏的監測大數據示例

                      Fig.  2  Examples of complete, fragment and sparse big data

                      圖  3  多源傳感器剩余壽命預測數模聯動解決方案與流程圖

                      Fig.  3  Idea and Flowchart of data-model interactive remaining useful life prediction with multi-source sensors

                      表  1  皮爾遜相關系數對比結果

                      Table  1  Comparative results of Pearson correlation coefficients

                      健康指標 皮爾遜相關系數
                      單一傳感器 低壓壓氣機出口總溫度 (T24) 0.6753
                      高壓壓氣機出口總溫度 (T30) 0.6440
                      低壓渦輪出口總溫度 (T50) 0.7816
                      高壓壓氣機出口總壓力 (P30) ?0.7615
                      高壓壓氣機出口靜壓 (Ps30) 0.8106
                      燃料流量與Ps30的比率 (phi) ?0.7897
                      旁路比率 (BRP) 0.7248
                      出血焓 (htBleed) 0.6731
                      高壓渦輪冷卻劑排放 (W31) ?0.7141
                      低壓渦輪冷卻劑排放 (W32) ?0.7167
                      本文數模聯動復合健康指標 0.9002
                      下載: 導出CSV

                      表  2  失效時刻健康指標值的方差比較

                      Table  2  Variance of health indices at failure time

                      健康指標 方差值
                      單一傳感器 低壓壓氣機出口總溫度 (T24) 0.0274
                      高壓壓氣機出口總溫度 (T30) 0.0176
                      低壓渦輪出口總溫度 (T50) 0.0140
                      高壓壓氣機出口總壓力 (P30) 0.0264
                      高壓壓氣機出口靜壓 (Ps30) 0.0154
                      燃料流量與Ps30的比率 (phi) 0.0206
                      旁路比率 (BRP) 0.0225
                      出血焓 (htBleed) 0.0435
                      高壓渦輪冷卻劑排放 (W31) 0.0220
                      低壓渦輪冷卻劑排放 (W32) 0.0317
                      復合健康指標[133] 0.0035
                      復合健康指標[137] 0.0101
                      本文數模聯動復合健康指標 0.0013
                      下載: 導出CSV

                      表  3  剩余壽命預測性能比較

                      Table  3  Comparative results in the performance of the remaining useful life prediction

                      預測方法 Socre Accuracy MSE
                      支持向量回歸方法[65] 449 70 %
                      基于案例的學習方法[71] 1389.26 44 %
                      基于案例的推理方法[72] 216 67 % 176
                      多目標深度置信網絡集成方法[86] 334.23 226.20
                      卷積神經網絡[92] 1287 340
                      循環神經網絡[96] 219 59 % 155
                      受限玻爾茲曼機+LSTM網絡[104] 231 157.75
                      基于長短時網絡的編碼-解碼器[106] 256 67 % 164
                      循環神經網絡+自編碼器[107] 245 70 %
                      基于多損失編碼器與卷積復合特征的兩階段深度學習方法[108] 208 133.86
                      深度置信網絡+后向傳播神經網絡+改進粒子濾波算法[140] 543 51 % 283
                      深度置信網絡+改進粒子濾波算法[140] 314 63 % 172
                      線性Wiener隨機過程方法 低壓壓氣機出口總溫度(T24) $1.32 \times 10 ^9$ 45 % 1193.76
                      高壓壓氣機出口總溫度(T30) $2.96 \times 10 ^7$ 32 % 1288.29
                      低壓渦輪出口總溫度(T50) 377.67 62 % 210.86
                      高壓壓氣機出口總壓力(P30) 5109.67 53 % 420.48
                      高壓壓氣機出口靜壓(Ps30) 1328.63 61 % 296.03
                      燃料流量與Ps30的比率(phi) 1442.09 57 % 325.20
                      旁路比率(BRP) $2.59 \times 10 ^4$ 48 % 501.06
                      出血焓(htBleed) 2847.74 30 % 669.43
                      高壓渦輪冷卻劑排放(W31) $4.92 \times 10 ^4$ 48 % 458.40
                      低壓渦輪冷卻劑排放(W32) 1564.21 46 % 427.19
                      本文數模聯動預測方法 95.87 81 % 68.29
                      注: 表中“—”表示原文中沒有計算并給出該指標值.
                      下載: 導出CSV
                      360彩票
                    • [1] Pecht M. Prognostics and Health Management of Electronics. John Wiley and Sons, Ltd, 2008.
                      [2] Si X S, Wang W B, Hu C H, Zhou D H. Remaining useful life estimation—A review on the statistical data driven approaches. European Journal of Operational Research, 2011, 213(1): 1?14 doi: 10.1016/j.ejor.2010.11.018
                      [3] 彭宇, 劉大同. 數據驅動的故障預測與健康管理綜述. 儀器儀表學報, 2014, 35(3): 481?495

                      Peng Yu, Liu Da-Tong. Data-driven prognostics and health management: a review of recent advances. Chinese Journal of Scientific Instrument, 2014, 35(3): 481?495
                      [4] Liao L X, K?ttig F. Review of hybrid prognostics approaches for remaining useful life prediction of engineered systems, and an application to battery life prediction. IEEE Transactions on Reliability, 2014, 63(1): 191?207 doi: 10.1109/TR.2014.2299152
                      [5] 喻勇, 司小勝, 胡昌華, 崔忠馬, 李洪鵬. 數據驅動的可靠性評估與壽命預測研究進展: 基于協變量的方法. 自動化學報, 2018, 44(2): 216?227

                      Yu Yong, Si Xiao-Sheng, Hu Chang-Hua, Cui Zhong-Ma, Li Hong-Peng. Data driven reliability assessment and life-time prognostics: a review on covariate models. Acta Automatica Sinica, 2018, 44(2): 216?227
                      [6] 施權, 胡昌華, 司小勝, 扈曉翔, 張正新. 考慮執行器性能退化的控制系統剩余壽命預測方法. 自動化學報, 2019, 45(5): 941?952

                      Shi Quan, Hu Chang-Hua, Si Xiao-Sheng, Hu Xiao-Xiang, Zhang Zheng-Xin. Remaining useful lifetime prediction method of controlled systems considering performance degradation of actuator. Acta Automatica Sinica, 2019, 45(5): 941?952
                      [7] 陸寧云, 陳闖, 姜斌, 邢尹. 復雜系統維護策略最新研究進展: 從視情維護到預測性維護. 自動化學報, 2021, 47(1): 1?17

                      Lu Ning-Yun, Chen Chuang, Jiang Bin, Xing Yin. Latest progress on maintenance strategy of complex system: from condition-based maintenance to predictive maintenance. Acta Automatica Sinica, 2021, 47(1): 1?17
                      [8] 袁燁, 張永, 丁漢. 工業人工智能的關鍵技術及其在預測性維護中的應用現狀. 自動化學報, 2020, 46(10): 2013?2030

                      Yuan Ye, Zhang Yong, Ding Han. Research on key technology of industrial artificial intelligence and its application in predictive maintenance. Acta Automatica Sinica, 2020, 46(10): 2013?2030
                      [9] Van Asselt M B A, Mesman J, van’tKlooster S A. Dealing with prognostic uncertainty. Futures, 2007, 39(6): 669?684 doi: 10.1016/j.futures.2006.11.011
                      [10] Hess A, Calvello G, Frith P, Engel S J, Hoitsma D. Challenges, issues and lessons learned chasing the“Big P”: real predictive prognostics part 2. In: Proceedings of the IEEE Aerospace Conference, Big Sky, MT. USA: IEEE, 2006.
                      [11] Smith G, Schroeder J B, Navarro S, Haldeman D. Development of a prognostics and health management capability for the Joint Strike Fighter. In: Proceedings of the 1997 IEEE Autotestcon Proceedings AUTOTESTCON’97. Anaheim, CA, USA: IEEE, 1997.
                      [12] Pecht M, Jaai R. A prognostics and health management roadmap for information and electronics rich systems. Microelectronics Reliability, 2010, 50(3): 317?323 doi: 10.1016/j.microrel.2010.01.006
                      [13] Brombacher A. Reliability prediction and ‘Deepwater Horizon’; lessons learned. Quality and Reliability Engineering International, 2010, 26(5): 397?397 doi: 10.1002/qre.1135
                      [14] Si X S, Li T M, Zhang Q, Hu C H. Prognostics for linear stochastic degrading systems with survival measurements. IEEE Transactions on Industrial Electronics, 2020, 67(4): 3202?3215 doi: 10.1109/TIE.2019.2908617
                      [15] Chen J L, Jing H J, Chang Y H, Liu Q. Gated recurrent unit based recurrent neural network for remaining useful life prediction of nonlinear deterioration process. Reliability Engineering and System Safety, 2019, 185: 372?382 doi: 10.1016/j.ress.2019.01.006
                      [16] Kundu P, Darpe A K, Kulkarni M S. Weibull accelerated failure time regression model for remaining useful life prediction of bearing working under multiple operating conditions. Mechanical Systems and Signal Processing, 2019, 143: 106302
                      [17] Qian Y, Yan R, Hu S. Bearing degradation evaluation using recurrence quantification analysis and Kalman filter. IEEE Transactions on Instrumentation and Measurement, 2014, 63(11): 2599?2610 doi: 10.1109/TIM.2014.2313034
                      [18] Jin X, Sun Y, Que Z, Wang Y, Tommy W S C. Anomaly detection and fault prognosis for bearings. IEEE Transactions on Instrumentation and Measurement, 2016, 65(9): 2046?2054 doi: 10.1109/TIM.2016.2570398
                      [19] Singleton R K, Strangas E G, Aviyente S. Extended Kalman filtering for remaining-useful-life estimation of bearings. IEEE Transactions on Industrial Electronics, 2015, 62(3): 1781?1790 doi: 10.1109/TIE.2014.2336616
                      [20] Liao L X. Discovering prognostic features using genetic programming in remaining useful life prediction. IEEE Transactions on Industrial Electronics, 2014, 61(5): 2464?2472 doi: 10.1109/TIE.2013.2270212
                      [21] Li N P, Lei Y G, Lin J, Ding S X. An improved exponential model for predicting remaining useful life of rolling element bearings. IEEE Transactions on Industrial Electronics, 2015, 62(12): 7762?7773 doi: 10.1109/TIE.2015.2455055
                      [22] Choi J H, An D, Gang J, Joo J, Kim N H. Bayesian approach for parameter estimation in the structure analysis and prognosis. In: Proceedings of the Annual Conference of the Prognostics and Health Management Society. Portland, USA: IEEE, 2010.
                      [23] An D, Choi J H. Improved MCMC method for parameter estimation based on marginal probability density function. Journal of Mechanical Science and Technology, 2013, 27(6): 1771?1779 doi: 10.1007/s12206-013-0428-9
                      [24] Paris P, Erdogan F. A critical analysis of crack propagation laws. Journal of Basic Engineering, 1963, 85(4): 528?533 doi: 10.1115/1.3656900
                      [25] Forman R G. Study of fatigue crack initiation from flaws using fracture mechanics theory. Engineering Fracture Mechanics, 1972, 4(2): 333?345 doi: 10.1016/0013-7944(72)90048-3
                      [26] Li Y, Billington S, Zhang C, Kurfess T, Danyluk S, Liang S. Adaptive prognostics for rolling element bearing condition. Mechanical Systems and Signal Processing, 1999, 13(1): 103?113 doi: 10.1006/mssp.1998.0183
                      [27] Li Y, Kurfess T R, Liang S Y. Stochastic prognostics for rolling element bearings. Mechanical Systems and Signal Processing, 2000, 14(5): 747?762 doi: 10.1006/mssp.2000.1301
                      [28] Li C J, Lee H. Gear fatigue crack prognosis using embedded model, gear dynamic model and fracture mechanics. Mechanical Systems and Signal Processing, 2005, 19(4): 836?846 doi: 10.1016/j.ymssp.2004.06.007
                      [29] Liang S Y, Li Y, Billington S A, Zhang C, Shiroishi J, Kurfess T R. Adaptive prognostics for rotary machineries. Procedia Engineering, 2014, 86: 852?857 doi: 10.1016/j.proeng.2014.11.106
                      [30] Oppenheimer C H, Loparo K A. Physically based diagnosis and prognosis of cracked rotor shafts. In: Proceedings of the Component and Systems Diagnostics, Prognostics, and Health Management Ⅱ, Orlando, FL, United States, 2002. 122−133.
                      [31] Marble S, Morton B P. Predicting the remaining life of propulsion system bearings. In: Proceedings of the 2006 IEEE Aerospace Conference. Big Sky, MT, USA: IEEE, 2006.
                      [32] Choi Y, Liu C R. Spall progression life model for rolling contact verified by finish hard machined surfaces. Wear, 2007, 262(1-2): 24?35 doi: 10.1016/j.wear.2006.03.041
                      [33] Liao L X, K?ttig F. A hybrid framework combining data-driven and model-based methods for system remaining useful life prediction. Applied Soft Computing, 2016, 44: 191?199 doi: 10.1016/j.asoc.2016.03.013
                      [34] Wang B, Lei Y G, Li N P, Li N B. A Hybrid prognostics approach for estimating remaining useful life of rolling element bearings. IEEE Transactions on Reliability, 2020, 69(1): 401?412 doi: 10.1109/TR.2018.2882682
                      [35] Cheng S, Pecht M. A fusion prognostics method for remaining useful life prediction of electronic products. In: Proceedings of the 2019 IEEE International Conference on Automation Science and Engineering Proc. Bangalore, India: IEEE, 2019, 102?107.
                      [36] Goebel K, Eklund N, Bonanni P. Prognostic Fusion for Uncertainty Reduction. Wright-Patterson AFB, OH, USA: Defense Technical Information Center, 2007.
                      [37] Bartram G, Mahadevan S. Prognostics and health monitoring in the presence of heterogeneous information. In: Proceedings of the Annual Conference of the Prognostics and Health Management Society 2012. Minneapolis, Minnesota, USA: IEEE, 2012.
                      [38] 裴洪, 胡昌華, 司小勝, 張建勛, 龐哲楠, 張鵬. 基于機器學習的設備剩余壽命預測方法綜述. 機械工程學報, 2019, 55(8): 1?13 doi: 10.3901/JME.2019.08.001

                      Pei Hong, Hu Chang-Hua, Si Xiao-Sheng, Zhang Jian-Xun, Pang Zhe-Nan, Zhang Peng. A Review of Machine Learning Based Remaining Useful Life Prediction Methods for Equipment. Journal of Mechanical Engineering, 2019, 55(8): 1?13 doi: 10.3901/JME.2019.08.001
                      [39] Khan S, Yairi T. A review on the application of deep learning in system health Management. Mechanical Systems and Signal Processing, 2018, 107: 241?265 doi: 10.1016/j.ymssp.2017.11.024
                      [40] Kim D E, Gofman M. Comparison of shallow and deep neural networks for network intrusion detection. In: Proceedings of the 2018 IEEE 8th Annual Computing and Communication Workshop and Conference. Las Vegas, NV, USA: IEEE, 2018, 1?5.
                      [41] 余凱, 賈磊, 陳雨強, 徐偉. 深度學習的昨天、今天和明天. 計算機研究與發展, 2013, 50(9): 1799?1804 doi: 10.7544/issn1000-1239.2013.20131180

                      Yu Kai, Jia Lei, Chen Yu-Qiang, Xu Wei. Deep learning: yesterday, today and tomorrow. Journal of Computer Research and Development, 2013, 50(9): 1799?1804 doi: 10.7544/issn1000-1239.2013.20131180
                      [42] Bishop C M. Pattern Recognition and Machine Learning. Springer, 2006.
                      [43] Ali J B, Chebel-Morello B, Saidi L, Malinowski S, Fnaiech F. Accurate bearing remaining useful life prediction based on Weibull distribution and artificial neural network. Mechanical Systems and Signal Processing, 2015, 56-57: 150?172 doi: 10.1016/j.ymssp.2014.10.014
                      [44] 李世科. 基于LM-BP 神經網絡的液壓支架頂梁疲勞壽命預測及應用. 中國礦業, 2019, 28(5): 92?96

                      Li Shi-Ke. Fatigue life prediction and application of hydraulic support roof beam based on LM-BP neural network. China Mining Magazine, 2019, 28(5): 92?96
                      [45] 邱曉梅, 隋文濤, 王峰, 張洪波, 金亞軍. 基于相關系數和BP神經網絡的軸承剩余壽命預測. 組合機床與自動化加工技術, 2019, 4: 63?65

                      Qiu Xiao-Mei, Sui Wen-Tao, Wang Feng, Zhang Hong-Bo, Jin Ya-Jun. Remaining life prediction of bearing based on correlation coefficient and BP Neural Network. Modular Machine Tool & Automatic Manufacturing Technique, 2019, 4: 63?65
                      [46] Gebraeel N, Lawley M, Liu R, Parmeshwaran V. Residual life predictions from vibration-based degradation signals: a neural network approach. IEEE Transactions on Industrial Electronics, 2004, 51(3): 694?700 doi: 10.1109/TIE.2004.824875
                      [47] Mahamada A K, Saon S, Hiyamaa T. Predicting remaining useful life of rotating machinery based artificial neural network. Computers and Mathematics with Applications, 2010, 60: 1078?1087 doi: 10.1016/j.camwa.2010.03.065
                      [48] Lim P, Goh C K, Tan K C. A novel time series-histogram of features (TS-HoF) method for prognostic applications. IEEE Transactions on Emerging Topics in Computational Intelligence, 2018, 2(3): 204?213 doi: 10.1109/TETCI.2018.2822836
                      [49] Drouillet C, Karandikar J, Nath C, Journeauxab A C, Mansorib M EI, Kurfessa T. Tool life predictions in milling using spindle power with the neural network technique. Journal of Manufacturing Processes, 2016, 22: 161?168 doi: 10.1016/j.jmapro.2016.03.010
                      [50] Ahmadzadeh F, Lundberg J. Remaining useful life prediction of grinding mill liners using an artificial neural network. Minerals Engineering, 2013, 53: 1?8 doi: 10.1016/j.mineng.2013.05.026
                      [51] Zhang Z, Wang Y, Wang K. Fault diagnosis and prognosis using wavelet packet decomposition, Fourier transform and artificial neural network. Journal of Intelligent Manufacturing, 2012, 24(6): 1213?1227
                      [52] 徐東輝. 車用鋰離子動力電池剩余壽命非線性組合預測研究. 北京師范大學學報(自然科學版), to be published.

                      Xu Dong-Hui. Study on nonlinear combination prediction of RUL state space time series of automotive Lithium-ion power batteries. Journal of Beijing Normal University (Natural Science), to be published
                      [53] 楊洋. 基于ARIMA和BP神經網絡組合模型的鋰電池壽命預測[Ph.D. dissertation]. 海南大學, 2020.

                      Yang Yang. Battery Life Prediction Based on ARIMA with BPNN[Ph.D. dissertation]. Hainan University, 2020.
                      [54] Bektas O, Jones J A, Sankararaman S, Roychoudhury I, Goebel K. A neural network filtering approach for similarity-based remaining useful life estimation. The International Journal of Advanced Manufacturing Technology, 2019, 101: 87?103 doi: 10.1007/s00170-018-2874-0
                      [55] Li Z X, Wu D Z, Hu C, Terpenny J. Terpenny J. An ensemble learning-based prognostic approach with degradation-dependent weights for remaining useful life prediction. Reliability Engineering and System Safety, 2019, 184: 110?122 doi: 10.1016/j.ress.2017.12.016
                      [56] Cortes C. Prediction of Generalization Ability in Learning Machines[Ph.D. dissertation]. University of Rochester, 1995.
                      [57] Vapnik V. The Nature of Statistical Learning Theory. New York, Springer, 1995.
                      [58] Benkedjouh T, Medjaher K, Zerhouni N, Rechak S. Health assessment and life prediction of cutting tools based on support vector regression. Journal of Intelligent Manufacturing, 2015, 26(2): 213?223 doi: 10.1007/s10845-013-0774-6
                      [59] Liu J, Zio E. An adaptive online learning approach for support vector regression: Online-SVR-FID. Mechanical Systems and Signal Processing, 2016, 76-77: 796?809 doi: 10.1016/j.ymssp.2016.02.056
                      [60] Khelif R, Chebel-Morello B, Malinowski S, Emna Laajili E, Fnaiech F, Zerhouni N. Direct remaining useful life estimation based on support vector regression. IEEE Transactions on Industrial Electronics, 2017, 64(3): 2276?2285 doi: 10.1109/TIE.2016.2623260
                      [61] Mao W, He J, Zuo M J. Predicting remaining useful life of rolling bearings based on deep feature representation and transfer learning. IEEE Transactions on Instrumentation and Measurement, 2020, 69(4): 1594?1608 doi: 10.1109/TIM.2019.2917735
                      [62] Soualhi A, Medjaher K, Zerhouni N. Bearing health monitoring based on Hilbert-Huang transform, support vector machine, and regression. IEEE Transactions on Instrumentation and Measurement, 2015, 64(1): 52?62 doi: 10.1109/TIM.2014.2330494
                      [63] Sun F Q, Li X Y, Liao H T, Zhang X K. A Bayesian least-squares support vector machine method for predicting the remaining useful life of a microwave component. Advances in Mechanical Engineering, 2017, 9(1): 168781401668596
                      [64] García-Nieto P J, García-Gonzalo E, Sánchez-Lasheras F, Cos Juez F J D. Hybrid PSO-SVM-based method for forecasting of the remaining useful life for aircraft engines and evaluation of its reliability. Reliability Engineering and System Safety, 2015, 138: 219?231 doi: 10.1016/j.ress.2015.02.001
                      [65] Racha K, Brigitte C M, Simon M, Laajili E, Fnaiech F, Zerhouni N. Direct remaining useful life estimation based on support vector regression. IEEE Transactions on Industrial Electronics, 2017, 64(3): 2276?2285 doi: 10.1109/TIE.2016.2623260
                      [66] Huang H Z, Wang H K, Li Y F, Zhang L L, Liu Z L. Support vector machine based estimation of remaining useful life: current research status and future trends. Journal of Mechanical Science and Technology, 2015, 29(1): 151?163 doi: 10.1007/s12206-014-1222-z
                      [67] Huang G, Zhu Q, Siew CK. Extreme learning machine: a new learning scheme of feedforward neural networks. In: Proceedings of the 2004 IEEE International Joint Conference on Neural Networks. Budapest, Hungary: IEEE, 2004. 985?990.
                      [68] Chaves I C, Paula M R P, Leite L G M, Gomes J P P, Machado J C. Hard disk drive failure prediction method based on a Bayesian network. In: Proceedings of the 2018 International Joint Conference on Neural Networks. Rio de Janeiro, Brazil: IEEE, 2018. 1?25.
                      [69] Wu D, Jennings C, Terpenny J, Gao R X, Kumara S. A comparative study on machine learning algorithms for smart manufacturing: Tool wear prediction using random forests. Journal of Manufacturing Science and Engineering, 2017, 139(7): 1?9
                      [70] Singh S K, Kumar S, Dwivedi J P. A novel soft computing method for engine RUL prediction. Multimedia Tools and Applications, 2017, 78(4): 4065?4087
                      [71] Wang T, Yu J, Siegel D, Lee J. A similarity-based prognostics approach for remaining useful life estimation of engineered systems. In: Proceedings of the IEEE International Conference on Prognostics and Health Management. Denver, CO, USA: IEEE, 2008. 1?6
                      [72] Ramasso E. Investigating computational geometry for failure prognostics. International Journal of Prognostics and Health Management, 2014, 5: 1?18
                      [73] Yu J, Tan M, Zhang H Y, Tao D C, Rui Y. Hierarchical deep click feature prediction for fine-grained image recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019, to be published.
                      [74] Wu S, Roberts K, Datta S, Du J C, Ji Z C, Si Y Q. Deep learning in clinical natural language processing: a methodical review. Journal of the American Medical Informatics Association, 2020, 27(3): 457?470 doi: 10.1093/jamia/ocz200
                      [75] Nassif A B, Shahin I, Attili I, Azzeh M, Shaalan K. Speech recognition using deep neural networks: a systematic review. IEEE Access, 2019, 7: 19143?19165 doi: 10.1109/ACCESS.2019.2896880
                      [76] 雷亞國, 楊彬, 杜兆鈞, 呂娜. 大數據下機械裝備故障的深度遷移診斷方法. 機械工程學報, 2019, 55(7): 1?8 doi: 10.3901/JME.2019.07.001

                      Lei Ya-Guo, Yang Bin, Du Zhao-Jun, Lv Na. Deep transfer Diagnosis method for machinery in big data era. Journal of Mechanical Engineering, 2019, 55(7): 1?8 doi: 10.3901/JME.2019.07.001
                      [77] Dulaimia A A, Zabihia S, Asifa A, Mohammadib A. A multimodal and hybrid deep neural network model for remaining useful life estimation. Computers in Industry, 2019, 108: 186?196 doi: 10.1016/j.compind.2019.02.004
                      [78] 周福娜, 高育林, 王佳瑜, 文成林. 基于深度學習的緩變故障早期診斷及壽命預測. 山東大學學報: 工學版, 2017, 47(5): 30?37

                      Zhou Fu-Na, Gao Yu-Lin, Wang Jia-Yu, Wen Cheng-Lin. Early diagnosis and life prognosis for slowly varying fault based on deep learning. Journal of Shandong University(Engineering Science), 2017, 47(5): 30?37
                      [79] Xia M, Li T, Shu T X, Wan J F, Silva C W, Wang Z R. A two-stage approach for the remaining useful life prediction of bearings using deep neural networks. IEEE Transactions on Industrial Informatics, 2018, 15(6): 3703?3711
                      [80] Ren L, Cui J, Sun Y Q, Chen X J. Multi-bearing remaining useful life collaborative prediction: a deep learning approach. Journal of Manufacturing Systems, 2017, 43: 248?256 doi: 10.1016/j.jmsy.2017.02.013
                      [81] Huang F, Zhang J, Zhou C, Wang Y H, Huang J S, Zhu L. A deep learning algorithm using a fully connected sparse autoencoder neural network for landslide susceptibility prediction. Landslides, 2019, 17(1): 217?229
                      [82] Lee S, Kim H J, Kim S B. Dynamic dispatching system using a deep denoising autoencoder for semiconductor manufacturing. Applied Soft Computing, 2020, 86: 105904 doi: 10.1016/j.asoc.2019.105904
                      [83] Balevi E, Andrews J G. Autoencoder-based error correction coding for one-bit quantization. IEEE Transactions on Communications, 2020, 68(6): 3440?3451 doi: 10.1109/TCOMM.2020.2977280
                      [84] Ma J, Su H, Zhao W, Liu B. Predicting the remaining useful life of an aircraft engine using a stacked sparse auto encoder with multilayer self-learning[Online], available: https://doi.org/10.1155/2018/3813029, June 1, 2021.
                      [85] 安華, 王國鋒, 王喆, 馬凱樂, 鐘才川. 基于深度學習理論的刀具狀態監測及剩余壽命預測方法. 電子測量與儀器學報, 2019, 33(9): 64?70

                      An Hua, Wang Guo-Feng, Wang Zhe, Ma Kai-Le, Zhong Cai-Chuan. Deep learning-based method for tool condition monitoring and remaining useful life prediction. Journal of Electronic Measurement and Instrumentation, 2019, 33(9): 64?70
                      [86] Zhang C, Lim P, Qin A K, Tan K C. Multi objective deep belief networks ensemble for remaining useful life estimation in prognostics. IEEE Transactions on Neural Networks and Learning Systems, 2016, 28(10): 2306?2318
                      [87] LeCun Y, Bengio Y. Convolutional networks for image, speech, and time-series[Online], available: https://www.researchgate.net/publication/2453996, June 1, 2021.
                      [88] Ren L, Sun Y, Wang H, Zhang L. Prediction of bearing remaining useful life with deep convolution neural network. IEEE Access, 2018, 6(99): 13041?13049
                      [89] Wang B, Lei Y G, Li N P, Yan T. Deep separable convolutional network for remaining useful life prediction of machinery. Mechanical Systems and Signal Processing, 2019, 130: 1?18 doi: 10.1016/j.ymssp.2019.05.001
                      [90] Zhu J, Chen N, Peng W W. Estimation of bearing remaining useful life based on multiscale convolutional neural network. IEEE Transactions on Industrial Electronics, 2018, 66(4): 3208?3216
                      [91] Liu R N, Yang B Y, Hauptmann AG. Simultaneous bearing fault recognition and remaining useful life prediction using joint loss convolutional neural network. IEEE Transactions on Industrial Informatics, 2019, 16(1): 87?96
                      [92] Babu G S, Zhao P, Li X L. Deep convolutional neural network based regression approach for estimation of remaining useful life. International Conference on Database Systems for Advanced Applications.TX, USA, April 16?19, 2016, 214?228.
                      [93] Yang B Y, Liu R N, Zio E. Remaining useful life prediction based on a double-convolutional neural network architecture. IEEE Transactions on Industrial Electronics, 2019, 66(12): 9521?9530 doi: 10.1109/TIE.2019.2924605
                      [94] Kwon S J, Hah S, Choi J H, Lim J H, Lee S E, Kim J. Remaining-useful-life prediction via multiple linear regression and recurrent information of 20AhLiNixMnyCo1-x-yO2 pouch cell. Journal of Electroanalytical Chemistry, 2020, 858: 113729 doi: 10.1016/j.jelechem.2019.113729
                      [95] Li X Q, Jiang H K, Xiong X, Shao H D. Rolling bearing health prognosis using a modified health index based hierarchical gated recurrent unit network. Mechanism and Machine Theory, 2019, 133: 229?249 doi: 10.1016/j.mechmachtheory.2018.11.005
                      [96] Gugulothu N, Vishnu TV, Malhotra P, Vig L, Puneet Agarwal P, Shroff G. Predicting remaining useful life using time series embeddings based on recurrent neural networks[Online], available: https://arxiv.org/abs/1709.01073, June 1, 2021.
                      [97] Miao H H, Li B, Sun C, Liu J. Joint learning of degradation assessment and RUL prediction for aero engines via dual-task deep LSTM networks. IEEE Transactions on Industrial Informatics, 2019, 15(9): 5023?5032 doi: 10.1109/TII.2019.2900295
                      [98] Wu Y T, Yuan M, Dong S P, Lin L, Liu Y Q. Remaining useful life estimation of engineered systems using vanilla LSTM neural networks. Neurocomputing, 2018, 275: 167?179 doi: 10.1016/j.neucom.2017.05.063
                      [99] Elsheikh A, Yacout S, Ouali M S. Bidirectional handshaking LSTM for remaining useful life prediction. Neurocomputing, 2019, 32(5): 148?156
                      [100] Zhang Y Z, Xiong R, He H W, Pecht M. Long short-term memory recurrent neural network for remaining useful life prediction of lithium-ion batteries. IEEE Transactions on Vehicular Technology, 2018, 67(7): 5695?5705 doi: 10.1109/TVT.2018.2805189
                      [101] Huang C G, Huang H Z, Li Y F. A bidirectional LSTM prognostics method under multiple operational conditions. IEEE Transactions on Industrial Electronics, 2019, 66(11): 8792?8802 doi: 10.1109/TIE.2019.2891463
                      [102] Yu Y, Hu C H, Si X S, Zheng J F, Zhang J X. Averaged Bi-LSTM networks for RUL prognostics with non-life-cycle labeled dataset. Neurocomputing, 2020, 402: 134?147 doi: 10.1016/j.neucom.2020.03.041
                      [103] Zhang C, Lim P, Qin A K, Tan K C. Multi objective deep belief networks ensemble for remaining useful life estimation in prognostics. IEEE Transactions on Neural Networks and Learning Systems, 2016, 28(10): 2306?2318
                      [104] Deutsch J, He D. Using deep learning-based approach to predict remaining useful life of rotating components. IEEE Transactions on Systems Man and Cybernetics Systems, 2017, 48(1): 11?20
                      [105] Ellefsen A L, Bj?rlykhaug, E, ?s?y, V, Ushakov S, Zhang H X. Remaining useful life predictions for turbofan engine degradation using semi-supervised deep architecture. Reliability Engineering and System Safety, 2019, 183: 240?251 doi: 10.1016/j.ress.2018.11.027
                      [106] Malhotra P, Vig TV, Ramakrishnan A, Anand G, Vig L, Agarwal P. Multi-sensor prognostics using an unsupervised health index based on LSTM encoder-decoder[Onlline]. available: https://arxiv.org/abs/1608.06154, June 1, 2021.
                      [107] Yu W N, Kim Y, Mechefske C. Analysis of different RNN autoencoder variants for time series classification and machine prognostics. Mechanical Systems and Signal Processing, 2021, to be published.
                      [108] Pillai S, Vadakkepat P. Two stage deep learning for prognostics using multi-loss encoder and convolutional composite feature. Expert Systems with Applications, 2021, 171: 114569 doi: 10.1016/j.eswa.2021.114569
                      [109] Ren L, Sun Y, Cui J, Zhang L. Bearing remaining useful life prediction based on deep autoencoder and deep neural networks. Journal of Manufacturing Systems, 2018, 48: 71?77 doi: 10.1016/j.jmsy.2018.04.008
                      [110] Kapur K C, Pecht M. Reliability Engineering. John Wiley, New Jersey, 2014.
                      [111] 文成林, 呂菲亞, 包哲靜, 劉妹琴. 基于數據驅動的微小故障診斷方法綜述. 自動化學報, 2016, 42(9): 1285?1299

                      Wen Cheng-Lin, Lv Fei-Ya, Bao Zhe-Jing, Liu Mei-Qin. A review of data driven-based incipient fault diagnosis. Acta Automatica Sinica, 2016, 42(9): 1285?1299
                      [112] Ye Z S, Xie M. Stochastic modelling and analysis of degradation for highly reliable products. Appl. Stochastic Models Bus, 2015, 31(1): 16?32 doi: 10.1002/asmb.2063
                      [113] 司小勝, 胡昌華, 周東華. 帶測量誤差的非線性退化過程建模與剩余壽命估計. 自動化學報, 2013, 39(5): 590?601

                      Si Xiao-Sheng, Hu Chang-Hua, Zhou Dong-Hua. Nonlinear degradation process modeling and remaining useful life estimation subject to measurement error. Acta Automatica Sinica, 2013, 39(5): 590?601
                      [114] 周東華, 魏慕恒, 司小勝. 工業過程異常檢測、壽命預測與維修決策的研究進展. 自動化學報, 2013, 39(6): 711?722

                      Zhou Dong-Hua, Wei Mu-Heng, Si Xiao-Sheng. A survey on anomaly detection, life Prediction and maintenance decision for industrial processes. Acta Automatica Sinica, 2013, 39(6): 711?722
                      [115] 韓中, 程林, 熊金泉, 劉滿軍. 大數據結構化與數據驅動的復雜系統維修決策. 自動化學報, 2018: 1?12

                      Han Zhong, Cheng Lin, Xiong Jin-Quan, Liu Man-Jun. Complex system maintenance decisions based on big data structuration and data-driven. Acta Automatica Sinica, 2018: 1?12
                      [116] Sato K I. Lévy Processes and Infinitely Divisible Distributions. Cambridge University Press, 1999.
                      [117] 裴洪, 胡昌華, 司小勝, 張正新, 杜黨波. 不完美維護下基于剩余壽命預測信息的設備維護決策模型. 自動化學報, 2018, 44(4): 719?729

                      Pei Hong, Hu Chang-Hua, Si Xiao-Sheng, Zhang Zheng-Xin, Du Ddang-Bo. Remaining life prediction information-based maintenance decision model for equipment under imperfect maintenance. Acta Automatica Sinica, 2018, 44(4): 719?729
                      [118] 任子強, 司小勝, 胡昌華, 王璽. 融合多傳感器數據的發動機剩余壽命預測方法. 航空學報, 2019, 40(12): 1?12

                      Ren Zi-Qiang, Si Xiao-Sheng, Hu Chang-Hua, Wang Xi. Remaining useful life prediction method for engine combining multi-sensors data. Acta Aeronautica et Astronautica Sinica, 2019, 40(12): 1?12
                      [119] Li N P, Gebraeel N, Lei Y G, Linkan B, Si X S. Remaining useful life prediction of machinery under time-varying operating conditions based on a two-factor state-space model. Reliability Engineering and System Safety, 2019, 186: 88?100 doi: 10.1016/j.ress.2019.02.017
                      [120] Zhang Z X, Si X S, Hu C H, Lei Y G. Degradation data analysis and remaining useful life estimation: a review on Wiener-process-based methods. European Journal of Operational Research, 2018, 271(3): 775?796 doi: 10.1016/j.ejor.2018.02.033
                      [121] Gebraeel N Z, Lawley M A, Li R, Ryan J K. Residual-life distributions from component degradation signals: A Bayesian approach. ⅡE Transactions, 2005, 37(6): 543?557
                      [122] Huang Z Y, Xu Z G, Wang W H, Sun Y X. Remaining useful life prediction for a nonlinear heterogeneous wiener process model with an adaptive drift. IEEE Transactions on Reliability, 2015, 64(2): 687?700 doi: 10.1109/TR.2015.2403433
                      [123] Si X S, Wang W B, Hu C H, Zhou D H, Pecht M. Remaining useful life estimation based on a nonlinear diffusion degradation process. IEEE Transactions on Reliability, 2012, 61(1): 50?67 doi: 10.1109/TR.2011.2182221
                      [124] Zhang J X, Hu C H, He X, Si X S, Liu Y, Zhou D H. A novel lifetime estimation method for two-phase degrading systems. IEEE Transactions on Reliability, 2019, 68(2): 689?709 doi: 10.1109/TR.2018.2829844
                      [125] Li T M, Pei H, Pang Z N, Si X S, Zheng J F. A sequential Bayesian updated Wiener process model for remaining useful life prediction. IEEE Access, 2019, 8: 5471?5480
                      [126] Si X S, Zhang Z X, Hu C H. Data-Driven Remaining Useful Life Prognosis Techniques: Stochastic Models, Methods and Applications. Springer-Verlag, Germany, 2017.
                      [127] Peng W W, Li Y F, Mi J H, Yu L, Huang H Z. Reliability of complex systems under dynamic conditions: a Bayesian multivariate degradation perspective. Reliability Engineering and System Safety, 2016, 153(1): 75?87
                      [128] Nelsen R B. An Introduction to Copulas. Second Edition, Springer, 2006.
                      [129] Pan Z, Balakrishnan N, Sun Q. Bivariate degradation analysis of products based on Wiener processes and copulas. Journal of Statistical Computation and Simulation, 2012, 83(7): 1?14
                      [130] Peng W W, Li Y F, Yang Y J, Zhu S P, Huang HZ. Bivariate analysis of incomplete degradation observations based on inverse Gaussian processes and copulas. IEEE Transactions on Reliability, 2016, 65(2): 1?16 doi: 10.1109/TR.2016.2572118
                      [131] 劉勝南, 陸寧云, 程月華, 姜斌, 邢琰. 基于多退化量的動量輪剩余壽命預測方法. 南京航空航天大學學報, 2015, 47(3): 360?366

                      Liu Sheng-Nan, Lu Ning-Yun, Cheng Yue-Hua, Jiang Bin, Xing Yan. Remaining lifetime prediction for momentum wheel based on multiple degradation parameters. Journal of Nanjing University of Aeronautics & Astronautic, 2015, 47(3): 360?366
                      [132] 張建勛, 胡昌華, 周志杰, 司小勝, 杜黨波. 多退化變量下基于Copula函數的陀螺儀剩余壽命預測方法. 航空學報, 2014, 35(4): 1111?1121

                      Zhang Jian-Xun, Hu Chang-Hua, Zhou Zhi-Jie, Si Xiao-Sheng, Du Dang-Bo. Multiple degradation variables modeling for remaining useful life estimation of gyros based on copula function. Acta Aeronautica et Astronautica Sinica, 2014, 35(4): 1111?1121
                      [133] Liu K B, Huang S. Integration of data fusion methodology and degradation modeling process to improve prognostics. IEEE Transactions on Automation Science and Engineering, 2016, 13(1): 344?354 doi: 10.1109/TASE.2014.2349733
                      [134] Kim M, Song C Y, Liu K B. A generic health index approach for multisensory degradation modeling and sensor selection. IEEE Transactions on Automation Science and Engineering, 2018, 16(3): 1426?1437
                      [135] Liu K B, Chehade A, Song C. Optimize the signal quality of the composite health index via data fusion for degradation modeling and prognostic analysis. IEEE Transactions on Automation Science and Engineering, 2016, 14(3): 1504?1514
                      [136] Yan H, Liu K B, Zhang X, Shi J J. Multiple sensor data fusion for degradation modeling and prognostics under multiple operational conditions. IEEE Transactions on Reliability, 2016, 65(3): 1416?1426 doi: 10.1109/TR.2016.2575449
                      [137] Liu K B, Gebraeel N Z, Shi J J. A data-level fusion model for developing composite health indices for degradation modeling and prognostic analysis. IEEE Transactions on Automation Science and Engineering, 2013, 10(3): 652?664 doi: 10.1109/TASE.2013.2250282
                      [138] Deutsch J, He M, He D. Remaining useful life prediction of hybrid ceramic bearings using an integrated deep learning and particle filter approach. Applied Sciences, 2017, 649(7): 1?17
                      [139] 彭開香, 皮彥婷, 焦瑞華, 唐鵬. 航空發動機的健康指標構建與剩余壽命預測. 控制理論與應用, 2020, 37(4): 713?720

                      Peng Kai-Xiang, Pi Yan-Ting, Jiao Rui-Hua, Tang Peng. Health indicator construction and remaining useful life prediction for aircraft engine. Control Theory and Applications, 2020, 37(4): 713?720
                      [140] Peng K X, Jiao R H, Dong J, Pi Y T. A deep belief network based health indicator construction and remaining useful life prediction using improved particle filter. Neurocomputing, 2019, 361: 19?28 doi: 10.1016/j.neucom.2019.07.075
                      [141] Hu C H, Pei H, Si X S, Du D B, Pang Z N, Wang X. A prognostic model based on DBN and diffusion process for degrading bearing. IEEE Transactions on Industrial Electronics, 2020, 67: 8767?8777 doi: 10.1109/TIE.2019.2947839
                      [142] Saxena A, Goebel K. C-MAPSS data set, NASA Ames Prognostics Data Repository, 2008.
                      [143] Saxena A, Goebel K, Simon D, Eklund N. Damage propagation modeling for aircraft engine run-to-failure simulation. In: Proceedings of the International Conference on Prognostics and Health Management, PHM. Denver, CO, USA: IEEE, 2008.
                      [144] 任子強. 融合多傳感器數據的隨機退化設備健康管理方法研究[Master dissertation]. 火箭軍工程大學, 2019.

                      Ren Zi-Qiang. Research on Health Management Method for Stochastic Degrading Equipment Via Integrating Multi-sensors Data[Master dissertation]. Rocket Force University of Engineering, 2019.
                      [145] Saha B, Goebel K. Battery Data Set. NASA Ames Prognostics Data Repository, NASA Ames, Moffett Field, CA, 2007. http://ti.arc.nasa.gov/project/prognos.
                      [146] Battery Research Data. Center for Advanced Life Cycle Engineering(CALCE), University of Maryland. http://calce.umd.edu/data.
                      [147] Peng W W, Ye Z S, Chen N. Bayesian deep-learning-based health prognostics toward prognostics uncertainty. IEEE Transactions on Industrial Electronics, 2020, 67(3): 2283?2293 doi: 10.1109/TIE.2019.2907440
                      [148] Wang B, Lei Y G, Yan T, Li N P, Guo L. Recurrent convolutional neural network: A new framework for remaining useful life prediction of machinery. Neurocomputing, 2020, 379: 117?129 doi: 10.1016/j.neucom.2019.10.064
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